The present invention generally relates to a method for accurate estimation of the noise and, therefore, the signal-to-noise ratio (SNR) for communication channels. SNR is an important parameter for the data communication channel, as it determines the channel capacity. The present invention also describes how the method applies to ADSL data modems.
Noise and signal-to-noise ratio (SNR) estimation are considered relatively straightforward tasks. However, when the SNR is small, significant systematic errors in measurement may result in over-estimation of SNR. A similar situation arises during runtime monitoring of the SNR. At this time, sufficient numbers of bits have already been assigned to each channel using QAM modulation scheme. Therefore, the SNR relative to the QAM lattice size depends on the noise margin and the desired bit error rate (BER). If a relatively small margin is desired, similar measurement errors may result in over-estimation of SNR. An additional problem that arises in these conditions is that the variance of the noise estimator (which is a Chi-Squared random variable under Gaussian assumptions) is relatively high. Therefore, the SNR estimates may vary by several dB, and there is typically only 50% confidence in the usual estimators that the actual SNR value will not be worse than that estimated.
FIG. 1 illustrates a simplified block diagram of an ADSL modem receiver 20, showing all the components of noise estimation. Typically, each symbol transmitted in such a system is first passed through a time domain equalizer (TEQ) 22, which is typically a linear filter designed for the purpose of minimizing inter-symbol interference. Then, the time series data is converted into a time series of vectors (or multiple channels), by taking a fourier transform 24 of the samples. A frequency equalizer (FEQ) 26 scales and rotates the complex FFT vectors so that they fall on the constellation points on the N-QAM modulation. Finally, the location of the signal is compared to the nearest constellation point (example of 4QAM is illustrated in FIG. 2), and the difference is taken as the noise for that channel. The noise and the signal-to-noise ratio (SNR) are measured using different setups during different stages of training. Initially, there is no information about the channel, and an initial trivial estimate of the TEQ and FEQ 26 are used. A 4-QAM periodic signal is used to establish timing recovery, and then to obtain the TEQ and FEQ filters. During this phase, trellis decoding is not active. For the purposes of SNR estimation, the signal is quantized to the nearest constellation point. The process is illustrated in FIG. 2.
The constellation points 30 are shown as the dark squares. The dots 32 are the data points belonging to the first quadrant. The pluses 34 are data points belonging to the second quadrant. However, when the noise is high, the signal crosses the lattice boundaries resulting in a detection error, and lower estimated noise. In this example, data signals in the shaded region 36 are ignored (or termed as erasures, indicating that they do not count towards a valid measurement). However, if the data signal is in the un-shaded region 38, first, it is quantized to the nearest constellation point 30. For example, a “plus” that has traveled from second quadrant to the first quadrant will be assumed to be arising from the first quadrant. The squared distance between the data and the nearest constellation point 30 serves as a measure of noise. An average is taken over a number of samples to get the average SNR. This causes a detection error (that will probably be corrected by the upper layers of the communication protocol), and in addition, it causes an error in the SNR estimation. Because of this reason, the SNR would be consistently over-estimated.
Once the proper TEQ and FEQ filters are in place and the cyclic prefix is introduced, the trellis decoding is turned off, or left off, based on the negotiated parameters between the modem and the DSLAM. However, still, in order to save memory, and in order to avoid latencies of the viterbi decoder, the SNR is typically measured using the quantization scheme described above. When higher order constellations are used, what matters for the purposes of measurement is not the overall SNR, but the relationship of the noise to Lattice size, that also governs the bit error rate and the noise margin. Therefore, the measurements get affected even at higher overall SNR.
An illustration of the consequences 40 for an ADSL modem is shown in FIG. 3, which is a simulation of downstream SNR measurement for 17.5 K feet loop with 150 feet bridge tap. Since noise outside the constellation points is either ignored or measured small due to neighboring constellation point, the measured SNR 42 is higher than the actual SNR 44. Errors in SNR measurement result in conservative design techniques such as use of a high noise margin.
The plot illustrated in FIG. 3 is derived through simulation of the ADSL handshake process. The X-axis 46 plots the various frequency bins. In each frequency bin, the SNR 42 is measured using the four QAM method, and plotted against the real SNR value 44 (as would be measured with prior information of constellation points). The calculated SNR 42 is generally higher than the real SNR 44, especially for SNR values below 10 dB. Around these SNR levels, there is also a larger tendency to see erasures 48, as the noise is larger than the constellation size. Another problem that can be inferred from this simulation is the variability in the estimates. As the SNR gets low, the variability in the measurement increases. Therefore, one has to follow more conservative schemes of allocating sufficient margins. Under such situations, it is more desirable to have an SNR estimate, such that one can be reasonably sure that the actual SNR is better than that value, without significantly underestimating the SNR.
Thus, basically, the existing problem is that when the signal to noise ratio is relatively small compared to the constellation size in a communication channel, there is a systematic error in estimation of the SNR. The only other known solution is to keep a record of all the data, and then use the expectation maximization algorithms to calculate the parameters for the mixture of gaussians. This one known solution, however, has its disadvantages, namely it needs too much computation and data storage.
The present invention, therefore, provides a simple solution to the problem of SNR measurement and a method to incorporate a level of confidence in the SNR estimates.
Therefore, an improved method for accurate estimation of noise for data modems is needed. The present invention provides such an improved method and explores a computationally efficient method for SNR estimation that also allows for specification of a confidence level in the estimates. The method of the invention solves the problem by incorporating information about the structure of the problem, and the collected statistic into the solution. Features and advantages of the present invention will become apparent upon a reading of the attached specification, in combination with a study of the drawings.